Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography

Venhuizen, Freerk G.; Ginneken, Bram van; Asten, Freekje van; Grinsven, Mark J. J. P. van; Fauser, Sascha; Hoyng, Carel B.; Theelen, Thomas; Sánchez, Clara I.

doi:10.1167/iovs.16-20541

Cited by 102 publications

(57 citation statements)

References 38 publications

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“…16 The automation of screening has been applied to macular degeneration with researchers using fundus photographs, OCT, or a combination to monitor patients for progression of AMD. [27][28][29] Scientists and researchers are also currently developing algorithms that use fundus auto-fluorescence to automatically detect progression of geographic atrophy in patients with dry AMD. 30 Home OCT (Notal Vision Ltd, Tel Aviv, Israel), a homebased OCT device which uses AI for automated detection of intraretinal and/or subretinal fluid in the central 10 degrees of eyes with exudative macular degeneration, received Breakthrough Device Designation by the FDA in 2018.…”

Section: Artificial Intelligencementioning

confidence: 99%

Advances in Telemedicine in Ophthalmology

Parikh

Armstrong

Liou

et al. 2020

Seminars in Ophthalmology

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Telemedicine is the provision of healthcare-related services from a distance and is poised to move healthcare from the physician's office back into the patient's home. The field of ophthalmology is often at the forefront of technological advances in medicine including telemedicine and the use of artificial intelligence. Multiple studies have demonstrated the reliability of tele-ophthalmology for use in screening and diagnostics and have demonstrated benefits to patients, physicians, as well as payors. There remain obstacles to widespread implementation, but recent legislation and regulation passed due to the devastating COVID-19 pandemic have helped to reduce some of these barriers. This review describes the current status of tele-ophthalmology in the United States including benefits, hurdles, current programs, technology, and developments in artificial intelligence. With ongoing advances patients may benefit from improved detection and earlier treatment of eye diseases, resulting in better care and improved visual outcomes.

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Section: Artificial Intelligencementioning

confidence: 99%

Advances in Telemedicine in Ophthalmology

Parikh

Armstrong

Liou

et al. 2020

Seminars in Ophthalmology

View full text Add to dashboard Cite

show abstract

“…Several digital image classification studies have already explored various automated and semiautomated techniques for AMD screening. [16][17][18][19][20][21][22][23][24] However, relatively few have considered using a pixel-based pattern recognition approach 25,26 to integrate results of current commercially available modalities in intermediate AMD. Thus, the aim of this study was to develop and apply a computational approach (multispectral pattern recognition) to statistically classify the spectral characteristics of normal eyes and lesions in AMD, and to subsequently appraise its clinical utility against expert grading.…”

mentioning

confidence: 99%

Multispectral Pattern Recognition Reveals a Diversity of Clinical Signs in Intermediate Age-Related Macular Degeneration

Nivison‐Smith

Assaad

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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“…A similar study using the European Genetic Database successfully graded AMD with similar accuracy to human graders. 23 …”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography

Sandhu

ElTanboly

Shalaby

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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PurposeWe determine the feasibility and accuracy of a computer-assisted diagnostic (CAD) system to diagnose and grade nonproliferative diabetic retinopathy (NPDR) from optical coherence tomography (OCT) images.MethodsA cross-sectional, single-center study was done of type II diabetics who presented for routine screening and/or monitoring exams. Inclusion criteria were age 18 or older, diagnosis of diabetes mellitus type II, and clear media allowing for OCT imaging. Exclusion criteria were inability to image the macula, posterior staphylomas, proliferative diabetic retinopathy, and concurrent retinovascular disease. All patients underwent a full dilated eye exam and spectral-domain OCT of a 6 × 6 mm area of the macula in both eyes. These images then were analyzed by a novel CAD system that segments the retina into 12 layers; quantifies the reflectivity, curvature, and thickness of each layer; and ultimately uses this information to train a neural network that classifies images as either normal or having NPDR, and then further grades the level of retinopathy. A first dataset was tested by “leave-one-subject-out” (LOSO) methods and by 2- and 4-fold cross-validation. The system then was tested on a second, independent dataset.ResultsUsing LOSO experiments on a dataset of images from 80 patients, the proposed CAD system distinguished normal from NPDR subjects with 93.8% accuracy (sensitivity = 92.5%, specificity = 95%) and achieved 97.4% correct classification between subclinical and mild/moderate DR. When tested on an independent dataset of 40 patients, the proposed system distinguished between normal and NPDR subjects with 92.5% accuracy and between subclinical and mild/moderate NPDR with 95% accuracy.ConclusionsA CAD system for automated diagnosis of NPDR based on macular OCT images from type II diabetics is feasible, reliable, and accurate.

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Automated Staging of Age-Related Macular Degeneration Using Optical Coherence Tomography

Cited by 102 publications

References 38 publications

Advances in Telemedicine in Ophthalmology

Advances in Telemedicine in Ophthalmology

Multispectral Pattern Recognition Reveals a Diversity of Clinical Signs in Intermediate Age-Related Macular Degeneration

Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography

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